management of distributed knowledge sources for complex application domains
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Management of Distributed Knowledge Sources for Complex Application Domains
Meike Reichle, Kerstin Bach, Alexander Reichle-Schmehl and Klaus-Dieter Althoff
University of Hildesheim
{lastname}@iis.uni-hildesheim.de
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Outline
• Motivation• Knowledge Modularization• Knowledge Map
• Classification of Knowledge Sources– Knowledge Source Properties
• Conclusion and Outlook
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Motivation
• Knowledge-based systems deal with increasingly complex application domains
• Distributed, knowledge-based systems – Distributed knowledge processing– Distributed knowledge acquisition
• Realisation of distributed, knowledge-based systems using well-known AI techniques
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The docQuery Project
• Travel Medicine– Prevention, management and research of travel
related medical aspects– Interdisciplinary: Requires expertise in other areas like
geography, activities, etc.
• Our main goal within the docQuery project– Provision of individualized and reliable information– On-demand query processing– Up-to-date information
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SEASALT
• Sharing Experiences using an Agent-based System Architecture LayouT
• Instantiation of the CoMES (Collaborative Multi-Expert-Systems) approach
• Features– Application-independent architecture
– Knowledge acquisition from a web-community
– Knowledge modularisation,
– Agent-based knowledge maintenance
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SE
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Knowledge Modularisation
• Knowledge Line (KL) within SEASALT– KL consists of complex knowledge in smaller,
reusable units (knowledge sources)
• Distribution of knowledge – Reflects structure of complex (interdisciplinary)
domains– Facilitates knowledge acquisition – Facilitates knowledge maintenance
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Knowledge Line
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Knowledge Sources
• Topic Agents + external sources• Contain different kinds of information
– Multiple knowledge sources for the same purpose
• Knowledge sources are accessed dynamically– according to their properties
• Retrieval results (can) serve as input for a subsequent query
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Knowledge Map: Motivation
• Term originates in Davenport’s and Prusak’s work on Working Knowledge1
• Organises all available knowledge sources– Who is the expert on a certain topic?
• Coordination Agent (Broker, Mediator)– Access to knowledge sources– Combines retrieved information– Uses Knowledge Map
1 Thomas H. Davenport and Laurence Prusak. Working Knowledge: How Organizations Manage What they Know. Harvard Business School Press, May 2000.
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Knowledge Map: Definition I
• Knowledge Map KM consists of a number of Knowledge Sources KS:
• A Knowledge Source KS consists of a knowledge base KB and an interface I:
KM= {KS1 , KS2 , KS 3 , .. . KS n }
KS= {KB, I }
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Knowledge Map: Definition II
• Dependencies between the Knowledge Sources– Input/Output dependencies enabling a subsequent retrieval
• Constraints on the Retrieval– Constraints over all Knowledge Sources
→ Availability, Costs, etc.
• Individual Retrieval Graph– Representing requested knowledge sources for an individual
query
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Knowledge Map: Example
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Computing Retrieval Graphs
• Computed based on – The information a user gives in an individual query– Pre-defined constraints– Knowledge Source dependencies
• A-priori computation of the retrieval path• Modified Dijkstra2 algorithm to determine an
optimal route over the graph2 Edsger W. Dijkstra. A note on two problems in connexion with graphs. NumerischeMathematik, 1:269–271, 1959.
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Classification of Knowledge Sources
• Different properties referring to– Meta-information – Content
• Complex Knowledge Source properties– Compound properties
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Meta-Properties• Access Limits:
– Number of requests per time unit, e.g. Projekt Deutscher Wortschatz3
• Format:– XML, HTML, data base tables, pure text, ...
• Syntax:– HTTP, SQL, agent, web service, ...
• Trust / Provenance: – Trustworthiness and reliability knowledge sources
3 http://wortschatz.uni-leipzig.de/
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Content Properties
• Content: – Semantic description: What knowledge is provided?
• Coverage: – How good is the knowledge source’s topic covered?
• Completeness: – How complete is the information offered?
• Up-to-dateness• Expiry
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Complex Knowledge Source Properties
• Complex properties– Compound properties as a (weighted) sum of
the presented simple properties
• Example: Quality– Comprises different aspectsQuality= 2× Coverage 2×Up−to−Dateness 2 × Answer Speed
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Assessment of Knowledge Source Properties
• Automatically assessable properties– Speed, language and structure
• Manually maintained properties– Knowledge engineer assigns property values
• Relations between properties– Syntax, format, structure and cardinality are partially
related basic sanity checks of their assigned values
• Similarity-based reasoning
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Values of Knowledge Source Properties
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Conclusion
• Knowledge modularisation:
Knowledge Line approach in SEASALT• Focus on distributed knowledge acquisition
Dynamic access and assessment of distributed knowledge sources
• Retrieval over distributed knowledge sources
• Management of distributed knowledge sources
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Outlook
• Retrieval Path computation– More flexible computation– Algorithm extension towards a more flexible and
subsequent result dependent routing– Automated integration of feedback about knowledge
sources
• Application and evaluation in docQuery
Thank you for your attention!
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